Operations Research
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Introduction to Algorithms
A language for modeling agents' decision making processes in games
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Learning social preferences in games
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Multi-agent influence diagrams for representing and solving games
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
On the complexity of constrained Nash equilibria in graphical games
Theoretical Computer Science
Methods for empirical game-theoretic analysis
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Pure Nash equilibria: hard and easy games
Journal of Artificial Intelligence Research
Journal of Artificial Intelligence Research
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In order to succeed, agents playing games must reason about the mechanics of the game, the strategies of other agents, other agentsý reasoning about their strategies, and the rationality of agents. This paper presents a compact, natural and highly expressive language for reasoning about the beliefs and rationality of agentsý decision-making processes in games. It extends a previous version of the language in a number of important ways. Agents can reason directly about the rationality of other agents; agentsý beliefs are allowed to conflict with one another, including situations in which these beliefs form a cyclic structure; agentsý play can deviate from the normative game theoretic solution. The paper formalizes the equilibria that holds with respect to agentsý models and behavior, and provides algorithms for computing it. It also shows that the language is strictly more expressive than that of Bayesian games.